Uneven urban-rural digital dividends: A cross-sectional study on how internet use exacerbates quality of life disparities among Chinese women in extreme poverty

Abstract

Objectives

Existing research has not sufficiently addressed the specific associations between internet use and the quality of life (QoL) for women experiencing extreme poverty, particularly in relation to urban–rural disparities. This study seeks to fill this gap by investigating how internet use is associated with QoL among Chinese women in extreme poverty and explores its implications for urban–rural inequality.

Methods

This cross-sectional study utilized data from 513 women living in extreme poverty from Chinese General Social Survey (CGSS) 2021. Analytical techniques included Ordinary Least Squares (OLS) regression, Recentered Influence Function (RIF) regression, and RIF decomposition.

Results

(1) Significant urban-rural QoL inequalities were found among women in extreme poverty. (2) Internet use was significantly associated with higher QoL for the overall sample and the urban subsample, but showed no significant association for the rural subsample. (3) Internet use was associated with widened urban-rural QoL disparities, particularly among women at medium and upper-middle QoL levels. (4) Decomposition analysis revealed that internet use accounted for a substantial proportion (64.33%) of the total observed urban-rural QoL disparities.

Conclusion

Digital empowerment policies for women in extreme poverty in rural areas have greater potential and value in the future.

Keywords

women in extreme poverty quality of life internet use urban-rural disparities Chinese context

1. Introduction

Digital transformation is profoundly affecting health well-being and quality of life (QoL) of individuals, but this impact varies significantly across groups. For women in extreme poverty, a particularly cross-vulnerable group, digital technologies can either be a new way to improve QoL or exacerbate the inequalities they face. World Health Organization data show that women in extreme poverty are generally disadvantaged in terms of access to health services, health literacy, and health management.¹ In China, this phenomenon is accentuated by urban-rural digital divide. Data from China Internet Network Information Center show that as of June 2024, the Internet penetration rate in cities and towns was 85.3%, while in rural areas it was only 63.8%, and women’s Internet usage rate continues to be lower than men’s.² This gender disparity in digital development and urban-rural divide may further exacerbate inequalities in access to health services and QoL disparities for women in extreme poverty.

China’s long-standing urban-rural dichotomy has led to significant disparities between urban and rural residents in terms of public services, infrastructure, and development opportunities. This structural gap is not only reflected in economic income, but also involves multiple dimensions such as educational resources, medical services, and social security.^3,4 It also profoundly impacts QoL, and in the context of digital transformation, disparities in urban-rural digital infrastructure and literacy may further widen the QoL gap between urban and rural residents.^5–7 Furthermore, rural areas face more barriers in terms of access to digital health resources and utilization of telemedicine services.^6–8

There remains a persistent gender digital divide worldwide, with women’s internet access and usage rates lagging behind those of men⁹。From the gender perspective, women in extreme poverty face more constraints in terms of access to resources, development opportunities, and social participation. Studies have found that structural factors such as gender discrimination, unequal access to education, and household responsibilities significantly affect female vulnerability to poverty.^10–14 In particular, regarding health service access, women in extreme poverty are disadvantaged not only in accessing primary care^15,16 but also are challenged in areas such as health literacy, disease prevention,^15,17 and health management capacity.¹⁸ Notably, the ubiquity of the Internet can improve the health and QoL for women in extreme poverty by enhancing health information access,^19,20 expanding healthcare resources,^19,21,22 and increasing health management participation.^20,23 These improvements can significantly enhance their health well-being.²⁴

A review of the extant literature on the impact of Internet use on health-related QoL reveals three predominant research areas. At the level of mechanism, Internet use has been demonstrated to exert an influence on individual QoL by means of enhancing health literacy, promoting the utilization of telemedicine services and enhancing health self-management capabilities.^{19,20,23,25,26} From the perspective of urban-rural differences, the urban-rural digital divide in Internet use is not only manifested in utilization rates,^27,28 but also in the ability to access health information.^27,29 In terms of the gender dimension, women tend to face more barriers to accessing and utilizing digital health resources, and this digital health gap is particularly pronounced in rural areas.^15–18 For women in extreme poverty in particular, Internet use may be an important way to improve their health status and QoL, but it may also exacerbate their health inequalities due to the urban-rural digital divide.

However, existing studies have paid less attention to the impact of Internet use on QoL for women in extreme poverty in terms of health and urban-rural disparities. Based on this, this study focuses on the group of women in extreme poverty and identifies four progressive research questions:(1) whether there is urban-rural disparities in QoL for women in extreme poverty; (2) whether Internet use affects the QoL for women in extreme poverty and compares the urban-rural sub-sample; (3) the effect of Internet use on QoL disparities for women in extreme poverty and on urban-rural disparity; and (4) Internet use effect decomposition affecting rural-urban disparities in QoL for women in extreme poverty.

2. Methods

2.1. Data and participants

The data used in this cross-sectional research come from the dataset of Chinese General Social Survey (CGSS) 2021 wave (https://cgss.ruc.edu.cn/English/Home.htm). CGSS is a dynamic research system on social structure change in China, which is implemented by the National Survey and Data Center (NSRC) of Renmin University of China. From 2003 to 2022, CGSS has conducted 15 annual surveys, successfully interviewing 162,036 people nationwide in China, of which 2021 wave is the 14th annual survey of CGSS, which was officially opened to the whole society on March 31, 2023. Only data from the CGSS 2021 wave were used in this study because the question items assessing QoL could not maintain continuity and were only measured in the CGSS 2021 wave online health information seeking, making longitudinal panel data analysis impossible.

The identification of women in extreme poverty was based on two indicators, income and housing, with the following specific identification criteria: (1) women; (2) average daily income below the global absolute poverty line set by United Nations Development Programme (UNDP), i.e., a daily income of 1.9 US dollars (about 12.31 RMB yuan), which reflects the minimum income needed to meet basic human needs^30–32; and (3) no owner-occupied housing in their current place of residence, and their current housing is not owned by their spouses, children, parents, or parents of their spouses. Based on this screening criteria, this study screened a sample of 513 women in extreme poverty from the CGSS 2021 wave dataset.

2.2. Measures

Quality of life (QoL). Referring to the quality of life scale developed by Flanagan³³ and Wang et al.'s measurements.³⁴ Similar approaches using large datasets such as CGSS and CFPS to construct QoL or well-being indices through multi-indicator aggregation have been adopted by numerous scholars examining Chinese social issues,^35–38 further validating the acceptability and practicality of this measurement method within the local research context. We followed previous studies in measuring QoL in five dimensions.^39–41 That is, physical and material well-being (annual gross income above average; physical health; have at least one type of insurance); relations with other people (socializing with friends; living a happy life; socializing with neighbors); social, community and civic activities (voting in village/residents’ committee elections; participation in trade unions); personal development and fulfillment (reading; satisfied with work; perceived increasing social outlets); and recreation (go to the movies; participate in cultural activities; hang out with friends). Each dimension was followed by the corresponding measure in parentheses, coding compliance with the item as a “1” and otherwise as a “0”. The coded results of the 14 items were then summed to obtain an index ranging from 0 to 14. The higher the value of the index, the higher the QoL.

Internet use. Respondents’ Internet use was measured using a single question, “In the past year, how much have you used the Internet?” Options included never, rarely, sometimes, often, and very often, which were coded in order from 1 to 5. Higher values indicated more frequent use of the Internet.

2.3. Control variables

In this study, control variables were selected from two aspects: socio-demographic characteristics and residence characteristics. Socio-demographic characteristics included age (continuous variable), marriage status (married, unmarried) and years of education (continuous variable). Residence characteristics included residence (rural, urban) and residence region (northeast China, western China, central China, eastern China). In addition, variables related to health status and economic status have been included in QoL and are not controlled for here to avoid potential problems such as multicollinearity.

2.4. Statistical analysis

According to the research objectives, this study first analyzed the overall sample with descriptive statistics using Stata17, and then verified whether there was any QoL disparities between urban and rural women in extreme poverty using the T test/χ2 test (Table 1). Then, the impact of Internet use on the QoL of urban and rural women in extreme poverty was tested using multinomial OLS models, including separate tests for the urban and rural samples (Table 2). In exploring the urban-rural disparities section, RIF (Recentered influence function) regression and RIF decomposition were chosen, the rationale of which can be found in the studies of Firpo et al.⁴² and Rios-Avila.⁴³ Based on RIF regressions, the impact of Internet use on the QoL disparities of women in extreme poverty was examined (Table 3), including the impact on the urban-rural disparities (Table 3). Based on RIF decomposition, the percentage contribution of Internet use to the impact of QoL disparities for women in extreme poverty was calculated (Table 4).

Table 1.

Descriptive statistics of all variables(N=513).

Variable	Type of statistics	Total sample (n = 513)	Rural sample (n = 280)	Urban sample (n = 233)	T Test/χ2 test
Quality of life (QoL)	Mean (SD)	6.04 (1.74)	5.88 (1.63)	6.22 (1.86)	-2.216*
Internet use	Mean (SD)	2.56 (1.68)	2.18 (1.56)	3.02 (1.71)	-5.801**
Age	Mean (SD)	56.33 (17.35)	59.61 (15.15)	52.39 (18.97)	4.790**
Marriage status					11.678**
Married	n (%)	57 (11.11%)	19 (6.79%)	38 (16.31%)
Unmarried	n (%)	456 (88.89%)	261 (93.21%)	195 (83.69%)
Years of education	Mean (SD)	6.46 (4.79)	5.39 (4.10)	7.74 (5.23)	-5.710**
Residence
Rural	n (%)	280 (54.58%)	280 (100%)
Urban	n (%)	233 (45.42%)		233 (100%)
Residence region					15.182**
Northeast China	n (%)	12 (2.34%)	4 (1.43%)	8 (3.43%)
Western China	n (%)	151 (29.43%)	94 (33.57%)	57 (24.46%)
Central China	n (%)	217 (42.30%)	126 (45.00%)	91 (39.06%)
Eastern China	n (%)	133 (25.93%)	56 (20.00%)	77 (33.05%)

Note. ** p<0.01, * p<0.05.

Table 2.

Regression results of Internet use’s impact on QoL for women in extreme poverty.

Variables	Overall sample	Rural sample	Urban sample
Internet use	0.212**	0.149	0.283**
	[0.090,0.334]	[-0.010,0.307]	[0.092,0.474]
Age	0.010	0.010	0.010
	[-0.002,0.023]	[-0.008,0.029]	[-0.007,0.028]
Marriage status (ref: unmarried)	-1.297**	-0.950	-1.497**
	[-1.877,-0.718]	[-1.914,0.014]	[-2.201,-0.793]
Years of education	0.052*	0.035	0.060
	[0.010,0.095]	[-0.025,0.096]	[-0.002,0.121]
Residence (ref: rural)	-0.030	0.000	0.000
	[-0.330,0.269]	[0.000,0.000]	[0.000,0.000]
Residence region (ref: western China)
Northeast China	0.557	1.491**	-0.117
	[-0.367,1.482]	[0.562,2.421]	[-1.328,1.095]
Central China	0.401*	0.453*	0.384
	[0.069,0.734]	[0.044,0.862]	[-0.166,0.935]
Eastern China	0.269	0.135	0.296
	[-0.120,0.657]	[-0.432,0.702]	[-0.259,0.852]
Constant	5.478**	5.387**	5.364**
	[4.433,6.523]	[3.831,6.944]	[3.894,6.834]
Observations	513	280	233
R²	0.164	0.086	0.252
F	13.326**	4.181**	14.842**

Note. reported are coefficients based on robust standard errors, with 95% confidence intervals in parentheses; ** p<0.01, * p<0.05.

Table 3.

RIF regression results for Internet use on overall QoL disparities and urban-rural disparities among women in extreme poverty.

Variables	Overall sample	Urban-rural disparities
Variables	Overall sample	10th quantile	25th quantile	50th quantile	75th quantile	90th quantile
Internet use	-0.010*	0.126	0.143	0.367**	0.231*	0.028
	[-0.018,-0.002]	[-0.017,0.269]	[-0.010,0.296]	[0.200,0.534]	[0.043,0.420]	[-0.222,0.278]
Age	-0.001	-0.001	0.011	0.021*	0.012	-0.011
	[-0.002,0.000]	[-0.015,0.014]	[-0.005,0.027]	[0.003,0.039]	[-0.009,0.033]	[-0.036,0.013]
Marriage status (ref: unmarried)	-0.033	-0.079	-0.695*	-0.854*	-1.943**	-3.033**
	[-0.069,0.002]	[-0.549,0.391]	[-1.299,-0.090]	[-1.580,-0.127]	[-2.876,-1.010]	[-4.492,-1.574]
Years of education	0.001	0.010	0.023	0.060	0.070*	0.077
	[-0.002,0.004]	[-0.038,0.058]	[-0.033,0.079]	[-0.002,0.121]	[0.009,0.132]	[-0.001,0.155]
Residence (ref: rural)	0.013	0.025	-0.101	-0.133	0.132	0.150
	[-0.009,0.034]	[-0.351,0.401]	[-0.484,0.282]	[-0.561,0.295]	[-0.305,0.570]	[-0.415,0.716]
Residence region (ref: western China)
Northeast China	-0.024	0.100	0.553	0.905	1.299	0.191
	[-0.076,0.027]	[-0.213,0.413]	[-0.368,1.474]	[-0.281,2.092]	[-0.347,2.945]	[-2.495,2.877]
Central China	-0.009	-0.056	0.428*	0.604*	0.673*	0.327
	[-0.032,0.013]	[-0.401,0.288]	[0.007,0.849]	[0.121,1.087]	[0.158,1.188]	[-0.326,0.979]
Eastern China	0.004	-0.430	0.266	0.496	0.540	0.092
	[-0.024,0.031]	[-0.908,0.047]	[-0.226,0.758]	[-0.053,1.046]	[-0.042,1.122]	[-0.652,0.836]
Constant	0.247**	4.115**	4.517**	4.392**	7.318**	11.334**
	[0.179,0.316]	[3.092,5.138]	[3.231,5.804]	[3.013,5.771]	[5.686,8.949]	[9.033,13.635]
Observations	513	513	513	513	513	513
R²	0.033	0.032	0.035	0.104	0.142	0.149
F	2.328**	3.257**	3.465**	10.018**	10.927**	5.156**

Note. reported are coefficients based on robust standard errors, with 95% confidence intervals in parentheses; ** p<0.01, * p<0.05.

Table 4.

QoL disparities’ RIF decomposition results by urban-rural for women in extreme poverty.

Variables	Coefficient	Standard error	LLCI	ULCI	Contribution (%)
Rural	4.255**	0.211	3.841	4.669
Urban	4.840**	0.266	4.318	5.362
Difference	-0.585	0.340	-1.252	0.082	100
Explained	-0.336	0.208	-0.744	0.071	57.46
Unexplained	-0.249	0.340	-0.916	0.418	42.54
Internet use	0.376*	0.169	0.045	0.707	-64.33
Age	-0.343*	0.158	-0.652	-0.035	58.70
Marriage status (ref: unmarried)	-0.272*	0.125	-0.518	-0.027	46.55
Years of education	-0.188	0.148	-0.477	0.102	32.05
Residence region (ref: western China)
Northeast China	0.035	0.038	-0.040	0.110	-5.98
Central China	0.044	0.048	-0.051	0.138	-7.48
Eastern China	0.012	0.077	-0.139	0.163	-2.06

Note. LLCI and ULCI denote the lower and upper limits of the 95% confidence interval, respectively. ** p<0.01, * p<0.05.

Additionally, regarding the handling of missing data, we adopted different strategies based on the nature of the variables. For all core variables in this study (such as items of QoL index, internet use, and urban/rural identity), we employed listwise deletion, excluding the small number of cases with missing values. For sporadic missing values in some control variables, we imputed them using the sample mean of that variable.

3. Results

3.1. Descriptive statistics of all variables

Table 1 presents the descriptive statistics of the overall sample, which consisted of a total sample of 513 women in extreme poverty, with an urban-rural distribution of 280 (54.58%) and 233 (45.42%), respectively. In terms of QoL, the mean value of the overall sample (M=6.04, SD=1.74) is higher than that of the rural sample (M=5.88, SD=1.63), but lower than that of the urban sample (M=6.22, SD=1.86), and the disparity between the urban and rural samples is verified by the t-test (T=-2.216, p<0.05), which proves that there exist significant disparities in the QoL of the urban and rural extreme poor women. In terms of Internet use, it still shows that the mean of the urban sample (M=3.02, SD=1.71) is higher than that of the overall sample (M=2.56, SD=1.68), and then both are higher than that of the rural sample (M=2.18, SD=1.56), and urban-rural samples’ Internet use disparity is significant validated (T=-5.801, p<0.01). In terms of control variables, there were significant urban-rural disparities in age, marriage status, years of education, and residence region, but there were significant rural-urban disparities in age.

3.2. Regression analysis results of internet use affect QoL for women in extreme poverty

Table 2 presents the results of OLS regressions analyzing the association between Internet use and QoL for women in extreme poverty, conducted separately for the overall, urban, and rural samples. The results show that after excluding the effects of multicollinearity and heteroskedasticity, Internet use is significantly and positively correlated with QoL in the overall sample (0.212, p<0.01, 95% CI [0.090, 0.334]), and urban sample (0.283, p<0.01, 95% CI [0.092, 0.474]), but not with the rural sample (0.149, p<0.10, 95% CI [-0.010, 0.307]). These findings indicate that Internet use is associated with higher QoL among women in extreme poverty in the overall sample and the urban subsample, but not in the rural subsample.

3.3. Internet use’s impact on rural-urban QoL disparities for women in extreme poverty

Table 3 reports the results of the RIF regression examining the association between Internet use and QoL disparities within the group of women in extreme poverty, as well as the urban-rural disparities at different quartiles. The analyses find that Internet use is significantly associated with QoL disparities among women in extreme poverty as a whole (0.010, p<0.05, 95% CI [-0.018, -0.002]). However, in terms of urban-rural disparities in the QoL for women in extreme poverty, the RIF regression showed that Internet use is significantly associated with widened urban-rural QoL disparities at the 50th quantile (0.367, p<0.01, 95% CI [0.200,0.534]), and 75th quantile (0.231, p<0.05, 95% CI [0.043,0.420]) for women in extreme poverty, and the association is strongest at the 50th quantile.

3.4. RIF decomposition results of internet use on urban-rural displaced women’s QoL disparities

Table 4 reports the results of the RIF decomposition (only explainable part) examining the association between internet use and urban-rural disparities in the QoL for women in extreme poverty. The results show that characteristic variables such as internet use explain 57.46% of the urban-rural QoL disparities for women in extreme poverty, with internet use (0.376, p<0.05, 95% CI [0.0455, 0.707]) contributing more than age (-0.343, p<0.05, 95% CI [-0.652, -0.035]) and marriage status (-0.272, p<0.05, 95% CI [-0.518, -0.027]), and its contribution percentage are 64.33%, 58.70%, and 46.55%, respectively. It can be seen that, on the one hand, internet use is associated with widened urban-rural QoL disparities for women in extreme poverty; however, on the other hand, two factors, ageing and marriage, are associated with reduced urban-rural QoL disparities for women in extreme poverty. In addition, other variables have no significant association with the urban-rural QoL disparities for women in extreme poverty.

In order to report the impact effect of Internet use on the QoL disparities for women in extreme poverty at different quantiles detailly, this study displays the contribution of Internet use in the RIF decomposition results in Figure 1. As shown in Figure 1, the effect of Internet use on the QoL disparities for women in extreme poverty only exhibits an exacerbating effect in the range of 45th quantile ∼80th quantile interval of the QoL level, but this exacerbating effect continues to weaken.

Figure 1.

Internet use’s contribution to QoL disparities at different quartiles by urban-rural for women in extreme poverty.

4. Discussion

This study examines the association between Internet use and urban-rural QoL disparities among women in extreme poverty. The findings reveal significant urban-rural QoL disparities. While Internet use is significantly associated with higher QoL for this demographic overall (including urban subsample), this positive association is not observed in the rural subsample. Notably, Internet use is associated with exacerbated existing disparities, accounting for 64.33% of the total urban-rural QoL disparities for women in extreme poverty. These results contribute to our understanding of QoL dynamics for this vulnerable group in the digital era.

4.1. Urban-rural QoL disparities for women in extreme poverty

There are significant urban-rural QoL disparities for women in extreme poverty, which is consistent with the expectations of social exclusion theory, suggesting that uneven distribution of social resources may be associated with disadvantages for rural women in extreme poverty.^44–48 This is also consistent with the findings of previous studies on differences in QoL between urban and rural residents.^49,50 The reason may be related to urban-rural development imbalances, which are reflected in gaps in infrastructure, public services, employment opportunities, income levels, social support, and medical living conditions. These imbalances may enable urban women in extreme poverty to have access to superior resources in healthcare and education,⁵¹ economic conditions,⁵² and social inclusion and well-being.^49,53,54

4.2. Urban-rural heterogeneity in the association between internet use and QoL for women in extreme poverty

More importantly, the association between Internet use and QoL for rural and urban women in extreme poverty shows significant heterogeneity. On the one hand, Internet use is significantly associated with higher QoL for women in extreme poverty as a whole and in the urban sample, suggesting that the Internet may serve as an important tool for urban women in extreme poverty in relation to information access, social support,⁵⁵ health services,⁵⁶ social connections, and increased employment opportunities that may help them better adapt to their new environment.⁵⁷ On the other hand, this study found that internet use was not statistically significantly associated with the QoL of rural women in extreme poverty. This result profoundly highlights the multi-layered and complex nature of the “digital divide”. It aligns with the conclusions drawn by Yang et al.⁵⁸ and Ma et al.,⁵⁹ who noted significant group heterogeneity in the associations of the internet, particularly its limited association with outcomes for low-educated, older rural women. For the women in extreme poverty in this study, they may have only crossed the initial “access gap”, yet remain deeply entrenched in the “usage gap” and “benefit gap”.⁶⁰

A possible explanation for urban-rural heterogeneity lies in the “digital divide” and “benefit divide”, suggesting structural differences in internet use purposes among extremely poor women in urban and rural areas. Rural women in extreme poverty, who tend to have lower educational attainment, weaker digital skills, and more limited social capital, may be more likely to focus their internet use on passive consumption and entertainment. In contrast, urban women in extreme poverty, who generally possess higher educational levels and greater access to resources, may be better equipped to utilize the internet for proactive information seeking, skill enhancement, and capital accumulation.^61–64 It should be emphasized that the discussion of the aforementioned mechanisms is based on theoretical inferences and existing literature. Since this study uses only frequency of use as a measure of Internet usage, it is not possible to directly test these mechanisms; therefore, they should be regarded as exploratory explanations rather than definitive conclusions. Constrained by digital literacy and accessible resources, their internet use may remain largely confined to basic, consumption-oriented activities, which may limit effective utilization for income generation, accessing critical public services, or health interventions. This may contribute to the observed patterns of technology use.⁶⁵ Simultaneously, it offers a possible explanation for how, despite seemingly similar “use frequency”, the substantive gap in “usage benefits” is associated with the QoL disparities between urban and rural areas. It also suggests that rural women in extreme poverty may lack the critical capabilities and opportunity bridges to translate digital access into tangible benefits, as suggested by this study. Additionally, this offers a nuanced perspective on previous research suggesting that Internet use is associated with improved women’s economic status, quality of life and cognitive ability.^20,58,66

4.3. Internet Use’s impact on urban-rural QoL disparities for women in extreme poverty

Another important finding is that Internet use is associated with a pronounced urban-rural disparities widening pattern among women in extreme poverty with medium and upper-middle QoL levels. This suggests that the “dividends” of Internet use are not evenly distributed, and that resource differences between urban and rural areas may contribute to a clear differentiation in the associations between Internet use and QoL across urban and rural areas,¹⁹ with the magnitude of the association for the QoL of urban women in extreme poverty being much greater than that of rural women in extreme poverty. Moreover, this finding also complements research on the digital divide,⁶⁷ by revealing differential associations of Internet use with different QoL levels.

RIF decomposition results further indicate that Internet use accounts for 64.33% of the urban-rural QoL disparities for women in extreme poverty, suggesting that Internet use is an important factor associated with the urban-rural QoL disparities for women in extreme poverty.^24–26 However, this contribution gradually diminishes as the quantile points increase, suggesting that there may be other factors more strongly associated with the high QoL group.

To analyze further, the RIF regression reveals that the association of Internet use with widened urban-rural gaps is concentrated among women with median QoL and above, suggesting that there is a systematic urban-rural divergence in the potential digital benefits for this relatively advantaged group. A possible mechanism lies in the dual dilemma of resource transformation blockage and demand mismatch. On the one hand, although women in the upper-middle QoL have the basic ability to try to utilize the Internet (e.g., access to health information, online skills training), the rural group encounters structural barriers—low-quality networks that limit functionality, a scarcity of localized content, and a lack of offline support for converting online resources into action, potentially leading to “idling digital potential”; On the other hand, there may be a demand-resource gap between rural women’s core “developmental needs” (e.g., chronic disease management, children’s education), which could benefit from the Internet, and the lack of appropriate digital services (e.g., specialized telemedicine, vocational education platforms) in the countryside. At the same time, rural women may be more vulnerable to online risks (disinformation, privacy violations), which could further affect the net benefits. It should be emphasized that the discussion of the aforementioned mechanisms is based on existing literature and theoretical inferences. Since the only measure of Internet use employed in this study is frequency of use, these mechanisms cannot be directly tested; therefore, they should be regarded as exploratory explanations rather than definitive conclusions. The Internet thus serves as a probe for exposing systemic disadvantages: among women with the highest potential for improvement, the historically existing rural-urban disparities in resource transformative capacity are reflected in technology use patterns, ultimately manifesting themselves in the observed widening of the QoL disparities between the middle and upper classes.

4.4. Theoretical contribution

This study also contributes to existing theories. Firstly, it expands the boundaries of the application of social exclusion theory in the study of urban and rural poor women by incorporating Internet use into the analytical framework. While traditional social exclusion theory focuses on exclusion in physical and human capital dimensions such as income and education,^68–70 this study shows that Internet use is associated with the difference in the QoL of urban and rural women in extreme poverty in the digital age, accounting for 67.828% of the observed disparities. This finding not only enriches the measurement dimension of social exclusion, but more importantly reveals how digital exclusion intersects with traditional social exclusion mechanisms to shape the QoL disparities between urban and rural women in extreme poverty.

Secondly, it deepens the application of technology embeddedness theory in the field of public health research. Established research tends to view technology as a neutral tool that automatically leads to public welfare improvements.^71–73 However, this study reveals a significant difference in the association between Internet use and women in extreme poverty across urban and rural areas: it shows a significant positive association in the rural sample but does not show a similar association in the urban sample. The finding supports the central argument of technology embeddedness theory that the impact of technology is deeply rooted in a particular social context.

Finally, this study also makes important additions to digital divide theory. Traditional digital divide theory focuses on differences in technology access (Tier 1 digital divide) and differences in the ability to use it (Tier 2 digital divide).^74–77 This study reveals a “cumulative effect” of the digital divide: instead of reducing the urban-rural divide, Internet use is associated with a more pronounced widening of disparities among the middle and upper-middle QoL levels. This suggests that the digital divide may be related to health inequality in more complex ways,^78–81 and that even at the same level of QoL, urban and rural groups may be newly differentiated in ways that are associated with Internet use.^19,24 It provides a new theoretical perspective for understanding the relationship between digital technology and health inequality.

4.5. Policy implication

This study finds that Internet use is associated with exacerbated urban-rural disparities among women in extreme poverty while being associated with improved quality of life for the overall sample, which suggests the need for future policies to precisely harmonize empowerment and equity. Targeted interventions for rural groups should be prioritized: accelerating rural high-speed broadband coverage and affordable smartphone penetration, as well as health-focused digital literacy training—focusing on access to authoritative medical information, telemedicine, and mental health resources—to directly serve the public health goal of improving health access. Simultaneously, highly relevant apps with dialectal adaptation and localized content need to be developed and embedded in the grassroots health system to ensure that digital resources are translated into substantive action.

Policies need to address specific barriers underlying the lack of significant association observed for the rural sample: providing device charging/storage support for the unstably housed, developing online services that meet their core needs (job search, childcare, legal assistance in one place), and strengthening synergistic mechanisms between online platforms and offline community centers, social workers, and free clinics to form a closed loop of support. Meanwhile, particular attention needs to be paid to the association between Internet use and widened urban-rural quality of life disparities for women in the upper middle class, so as to break down the barriers to transforming their digital “potential” into better lives by improving the quality of rural networks, providing accurate localized content, and building support networks for sustained use.

These efforts are deeply consistent with the core of China’s “Internet+” strategy, especially the “Internet + Healthcare” and “Internet + Social Services” directions, and can be efficiently implemented under the policy framework of “Digital Countryside” construction. Currently, initial successes have been achieved in numerous local innovation initiatives. For instance, the “Smart Mobile Hospital” model promoted in areas like Jingning, Zhejiang, delivers digital health services, including remote consultations and medical insurance payments, directly to mountain villages through mobile clinics equipped with AI-assisted diagnosis systems. This effectively bridges the geographical gap in healthcare resources between urban and rural areas. Simultaneously, as demonstrated by the “Digital Magnolia” series of projects, a comprehensive empowerment model combining the “Magnolia Boost” public welfare health insurance for rural women with the “AI Bean Project” digital skills training not only provides immediate health protection but also helps them gain access to local digital employment opportunities. This creates a virtuous cycle, moving from “access” to “application” and ultimately to “benefit”.

From a global perspective, these practices are also closely aligned with the strategic direction advocated by WHO to leverage digital and intelligent technologies to empower primary healthcare and advance universal health coverage (UHC). Future policy design can draw upon the experience of such integrated interventions and reference monitoring frameworks like WHO’s “UHC Watch” to assess service accessibility and equity. This approach will foster a more systematic, evaluable Chinese solution that effectively translates digital potential into tangible improvements in quality of life.

4.6. Limitations

There are still some limitations to this study. Firstly, the study explored situations in Eastern cultures and Chinese scenarios, and there may be limitations in the applicability of the findings. Secondly, the representativeness of the study sample may be limited, and there is a need to validate the findings on a larger scale and examine the long-term effects through longitudinal studies. Finally, due to the use of secondary cross-sectional data, the study fails to establish strict causality and limits in-depth exploration of the impact mechanisms; and the use of a single item (frequency of use) to measure the complex behavior of “internet use” also represents a limitation of this study. Furthermore, the measurement of quality of life employs a composite approach using multiple indicators to construct a QoL index, which has limitations compared to utilizing standardized scales that have undergone comprehensive psychometric validation. Meanwhile, the Internet use indicators used in this study are derived from a single item in the CGSS questionnaire, and thus cannot distinguish the specific purposes of Internet use, nor can they capture respondents’ digital literacy or usage contexts.

5. Conclusions

In this study, empirical analyses reveal the complex associations between Internet use and QoL and disparities between rural and urban areas for women in extreme poverty. It found that Internet use is associated with improved QoL for women in extreme poverty, especially rural women in extreme poverty. However, it is also associated with exacerbated QoL urban-rural disparities among women in extreme poverty, particularly significant among samples with medium to high QoL levels. These findings provide new perspectives for understanding the implications of digital development for social equity, as well as a basis for targeted policies. In the future, more effective measures need to be taken to promote equitable access to and effective use of Internet resources, and to help rural women in extreme poverty make better use of the Internet to improve their QoL.

Footnotes

Acknowledgments

The authors are grateful to the China Survey and Data Center of Renmin University of China and its personnel for data assistance and support, and to the organizers and participants of the Chinese General Social Survey project team.

ORCID iDs

Yangyang Wang

Xuanxuan Tan

Ethical consideration

This research makes use of open, anonymized secondary data that does not contain any identifiable personal data and does not constitute research involving human subjects. Nonetheless, this research implementation process was subject to Ethics Committee of Soochow University review and approval (SUDA20240830H04).

Author contributions

Y.W: Model analyses, Data curation, Writing—original draft, Funding acquisition. X.T: Framework, Writing—original draft, Acquisition of data. Both authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by Social Science Youth Foundation of Jiangsu Province [24ZHC007].

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data supporting the results of this study were obtained from the Chinese National Survey Data Archive (CNSDA). The authors have been granted permission to use but not to share the data. However, these data are available upon reasonable request on the CNSDA website at https://www.cnsda.org/index.php?r=projects/view&id=65635422.

Disclosure

The authors report no conflicts of interest in this work.

Guarantor

XT.

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